We address the problem of fundamental performance limitations in adaptive parameter estimation and systemidentification, occurring in environments where perturbations are present but there is lack of sufficient excit...
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We address the problem of fundamental performance limitations in adaptive parameter estimation and systemidentification, occurring in environments where perturbations are present but there is lack of sufficient excitation. We construct a simple yet general bursting scenario to derive an analytical lower bound on the worst-case peak steady-state error for a wide class of parameter estimation and system identification algorithms. Our results show that in the absence of any input constraints, arbitrarily small perturbations impose a serious performance limitation, in the sense that the worst-case performance deteriorates proportionally with the size of the parametric uncertainty set.
Model accuracy is the most important step towards efficient control design. Various systemidentification techniques exist which are used to identify model parameters. However, these techniques have their merits and d...
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ISBN:
(纸本)9781728134734
Model accuracy is the most important step towards efficient control design. Various systemidentification techniques exist which are used to identify model parameters. However, these techniques have their merits and demerits which need to be considered before selecting a particular systemidentification technique. In this paper, we compared different types of systemidentification techniques and used them to identify our DC-motor use-case. Using the identified system, we designed different discrete PI controllers in order to investigate the system response. We concluded EKF provided the best performance in terms of parameter accuracy and convergence rate.
In this paper, we address the task of discrete-time modeling of nonlinear dynamic systems. We use Takagi-Sugeno fuzzy models built by LOLIMOT and SUHICLUST, as well as ensembles of LOLIMOT fuzzy models to accurately m...
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In this work, a recursive systemidentification algorithm is extended to improve reliability and better handle stochastic disturbances, measurement noise, and other adverse phenomena. The proposed approach involves th...
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